Missing Wedge Inpainting and Joint Alignment in Electron Tomography through Implicit Neural Representations

TL;DR

This paper introduces a self-supervised implicit neural representation method for electron tomography that effectively addresses missing wedge artifacts, data misalignment, and denoising, enabling high-quality 3D reconstructions from limited datasets.

Contribution

It presents a novel INR-based approach that performs inline alignment, inpainting, and denoising without requiring training data, improving generalization and efficiency in electron tomography reconstruction.

Findings

Produces high-quality tomograms from limited data

Handles diverse materials and experimental conditions

Requires minimal user input and preprocessing

Abstract

Electron tomography is a powerful tool for understanding the morphology of materials in three dimensions, but conventional reconstruction algorithms typically suffer from missing-wedge artifacts and data misalignment imposed by experimental constraints. Recently proposed supervised machine-learning-enabled reconstruction methods to address these challenges rely on training data and are therefore difficult to generalize across materials systems. We propose a fully self-supervised implicit neural representation (INR) approach using a neural network as a regularizer. Our approach enables fast inline alignment through pose optimization, missing wedge inpainting, and denoising of low dose datasets via model regularization using only a single dataset. We apply our method to simulated and experimental data and show that it produces high-quality tomograms from diverse and information limited datasets. Our results show that INR-based self-supervised reconstructions offer high fidelity reconstructions with minimal user input and preprocessing, and can be readily applied to a wide variety of materials samples and experimental parameters.